How Machine Learning can be an Asset for DevOps Processes

By StartUp City | Thursday, April 04, 2019

DevOps technology has been the cornerstone for software development for many organizations over the years. DevOps tools ensure a streamlined software development process by enabling communication and collaboration between development and operations teams. Organizations are integrating many emerging technologies like Machine learning (ML) to improve the efficiency of DevOps processes.

DevOps processes may generate a considerable amount of data through multiple methodologies and troubleshooting queries. Developers have to sort through all these data sets to find meaningful information. Sorting manually can result in confirmation bias as developers already make up their mind about the most relevant data for the DevOps environment. Machine learning technology uses algorithms to analyze a data set, which can help immensely in extracting meaningful information from a data set.

Decision Making: Human workforce cannot handle the quantitative analytics of massive data sets, as it exceeds their neural limitations. Developers focus on data thresholds to gain insights into the various data sets. However, a significant amount of information can be missed with this process as there might be actionable insights between the thresholds. ML tools can prove to be an asset for organizations as these tools make decisions based on a granular analysis rather than the visibility of the data sets. Ml tools iterate through all the data points that can be overlooked by developers.

Trend Observation: DevOps tools are capable of handling data from various channels of an organization. These tools can make inferences from the data sets that are not logically connected. Machine learning tools can help DevOps teams in recognizing patterns between data sets by recognizing trends across data sets.

Error Reduction: DevOps teams find it extremely difficult to deduce the contextual meaning behind an error. Although most DevOps teams document their mistake for future references, the information can sometimes be limited. Errors can be fatal for the efficiency of a business process, which can result in decreased productivity. ML tools continuously track the DevOps operations to identify any mistake that helps to improve the production models to keep any error from reoccurring.